The New Age of Marketing Measurement: AI Incrementality Testing

In today's complex marketing landscape, accurately measuring the true impact of marketing campaigns has become a top priority. With growing privacy restrictions, the decline of cookie-based tracking, and the need for data-driven decision-making, marketers are turning to advanced tools for help. Incrementality testing has become a popular choice for assessing the genuine lift generated by marketing activities, but it's often complex, time-consuming, and challenging to implement correctly. That’s where AI-powered incrementality testing steps in, simplifying the process and making it more accessible than ever.

With the latest update to Stella, our platform now includes an AI Agent trained by top marketing measurement experts to assist users through every phase of incrementality testing. This makes it easier for media buyers, from junior to senior levels, to set up, execute, analyze, and act on incrementality studies with confidence.

Why AI is Revolutionizing Incrementality Testing

AI has transformed incrementality testing in marketing by providing a scalable, efficient, and accurate way to evaluate campaign impact. Here’s how AI is reshaping the way marketers approach incrementality testing:

1. Optimized Experimental Design and Execution

Setting up a sound experiment is critical for reliable incrementality testing. AI algorithms excel in optimizing every aspect of the experimental design:

Ideal Timing: AI can analyze patterns in the consumer journey to determine the best time to initiate tests, maximizing the relevance and accuracy of the results.
Efficient Distribution: By optimizing the distribution of test groups, AI ensures balanced test and control samples, which reduces selection bias.
Duration Determination: Based on historical performance data, AI can calculate the optimal experiment length, ensuring results reach statistical significance without unnecessary delays.
Real-Time Anomaly Detection: AI models identify anomalies during testing—like sudden shifts in user behavior or seasonal changes—allowing marketers to make adjustments to maintain accuracy.

These features help streamline the entire experimental process, taking out much of the guesswork and reducing errors, especially in complex campaigns with multiple touchpoints.

2. Enhanced Data Processing and Analysis

AI brings unprecedented power to data processing, enabling incrementality tests to handle vast and varied datasets, from campaign metrics to consumer behavior:

Cross-Channel Data Integration: AI-powered models can process performance data across various channels, providing a holistic view of a campaign’s incremental impact.
High-Volume Data Analysis: With the ability to process results from thousands of experiments across multiple brands, AI provides a robust analysis that captures trends and insights traditional methods might overlook.
Behavioral and Demographic Insights: AI uses demographic and behavioral information to refine incrementality measurements, offering marketers nuanced insights that support more tailored campaign adjustments.

This advanced data processing not only enhances the accuracy of incrementality testing but also enables faster analysis, providing actionable insights when they’re needed most.

3. Real-Time Optimization for Budget Allocation

AI-powered incrementality testing doesn’t just measure past performance; it actively optimizes ongoing campaigns. With AI-driven tools, marketers can adjust campaigns in real-time to improve ROI:

Dynamic Budget Adjustments: AI models can simulate various budget scenarios, showing how incremental impact changes with budget shifts. This allows marketers to allocate resources to the most effective channels.
Scenario Planning: AI lets marketers optimize based on objectives like conversions, revenue, ROAS, or CPA, enabling them to adjust campaigns for maximum incremental value.
Preventing Diminishing Returns: By continuously analyzing performance data, AI can detect when a campaign has reached its peak effectiveness, helping to avoid ad fatigue and ensure continued incremental lift.

Real-time optimization is a game-changer for marketing teams, allowing them to adjust campaigns based on incremental impact and move away from vanity metrics, focusing on meaningful business outcomes.

4. Enhanced Accuracy Through Synthetic Control Groups

Creating accurate control groups is one of the biggest challenges in incrementality testing. AI addresses this with synthetic control groups that generate a more reliable baseline:

Aggregated Historical Data: AI models use historical attribution data, ad spend records, and even external variables like economic conditions to construct accurate synthetic control groups.
Mitigating External Factors: By accounting for variables such as seasonality, competitor activity, or weather changes, AI-powered synthetic controls provide a realistic baseline without needing to pause entire campaigns.
Reduced Bias: By continuously analyzing these variables, AI minimizes the chance of bias creeping into the control group, which results in a more accurate measure of the true incremental impact.

This feature eliminates the need for traditional holdout groups, which can disrupt revenue flow, making incrementality testing less intrusive and more reliable.

5. Continuous Learning and Adaptation

AI is not static; it evolves with each test. Through machine learning, AI-powered incrementality models improve over time:

Iterative Model Refinement: AI can iteratively refine its own parameters based on past results, allowing for more accurate predictions in future tests.
Identification of Key Variables: Advanced machine learning identifies demographic characteristics, purchase history, and online behavior that contribute most to incremental lift.
Ongoing Data Cleansing: AI automatically cleans and normalizes datasets, ensuring data accuracy and reducing inconsistencies that could otherwise skew results.

This self-learning capability enables AI models to provide marketers with continuously refined insights, making each incrementality study more effective than the last.

Introducing Stella's AI Agent: A New Standard in Incrementality Testing

With the recent update, Stella’s new AI Agent brings all the benefits of AI incrementality testing into a user-friendly platform. From setup to execution to actionable insights, the Stella AI Agent is designed to make incrementality testing accessible, effective, and actionable for media buyers at all experience levels. Here’s a closer look at how it works:

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Step 1: Study Form – Simplified Setup

Incrementality studies have traditionally been complex to set up. In the Study Form stage, the Stella AI Agent guides you through each field, providing explanations and suggestions:

Naming and Customization: Easily name and configure your study with guidance on which fields to prioritize.
Expert Recommendations: If you’re unsure about which variables to test, the AI provides personalized suggestions, tailored to your campaign goals and channels.
Real-Time Support: If questions arise, like “What type of study should I run if I want to test the effectiveness of Google shopping ads?”, Stella can provide an answer instantly.

This feature empowers users to set up incrementality tests with confidence, ensuring that even complex tests are configured correctly.

Step 2: Location Selection – Intelligent Holdout Regions

Choosing the right holdout regions is essential for achieving statistically significant results. Stella’s AI uses the last 90 days of data to identify the best regions for testing:

Automated Analysis: Stella AI evaluates revenue by date and location, suggesting holdout regions that require the smallest investment and shortest test duration.
Three Optimized Options: The platform provides three potential region choices, allowing marketers to choose the option best suited for their objectives.
Expert Validation: Users can ask, “What option is the best?” or “Are there any alternatives?” to get tailored recommendations from the AI.

By selecting ideal locations, Stella helps marketers avoid bias and ensures their results are statistically significant, even on smaller budgets.

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Step 3: Post-Test Analysis – Guided Insights and Recommendations

Once your test is complete, Stella’s AI dives into the data to provide a clear understanding of the results:

Custom Data Models: Stella runs custom models and synthetic controls to calculate iROAS (incremental Return on Ad Spend) within a 90% confidence level.
Actionable Insights: If users wonder, “What do I do with this iROAS number?”, the AI breaks down exactly what it means and suggests changes for the ad account or broader strategy.
Data Visualization Interpretation: The AI reads charts and offers interpretations, helping users understand the implications of each data point without needing to be data scientists.

This post-test analysis takes the guesswork out of incrementality testing, making it easy to understand, even for junior media buyers.

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Step 4: Dashboard – Holistic Campaign Management

Stella’s Dashboard provides an overarching view of campaign performance, allowing marketers to track ongoing tests, analyze past results, and make strategic adjustments:

Cross-Test Insights: Track multiple studies simultaneously, understanding how each campaign impacts the overall marketing strategy.
Next-Test Recommendations: Stella AI can suggest what to test next based on past results, enabling a continuous optimization loop that grows more effective over time.
External Factor Consideration: The AI can even recommend which external factors, like seasonal shifts or economic trends, to account for, ensuring that tests remain accurate and results meaningful.

The Dashboard ensures that marketers can see the big picture, making informed decisions that align with their long-term goals.

Why Choose Stella for AI-Powered Incrementality Testing?

Stella is more than just an incrementality testing tool; it’s a full-fledged measurement solution powered by AI, designed specifically to help mid-market eCommerce brands access the benefits of incrementality testing. Our goal is to make high-quality, data-driven marketing accessible to every brand, regardless of budget or experience level.

Key Benefits of Stella’s AI-Powered Incrementality Testing

User-Friendly and Accessible: Stella simplifies complex incrementality studies, making it easy for anyone to set up, interpret, and act on results.
Precision with Minimal Investment: Stella’s AI helps marketers achieve statistically significant results with the least investment possible, maximizing ROI.
Custom-Tailored Insights: The AI Agent provides step-by-step guidance and real-time answers to questions, from “What does this iROAS mean?” to “What should I test next?”
Comprehensive Data Analysis: Stella goes beyond testing channels; it also supports tactic-level tests and provides recommendations that drive strategy.
Holistic Approach: Designed to complement other measurement tools, Stella ensures brands get a full view of performance with incrementality testing and media mix modeling.

Embrace the Future of Marketing Measurement with Stella

The new Stella AI Agent is changing the way brands approach incrementality testing. By harnessing the power of AI, Stella makes it possible to accurately measure campaign impact without the hassle, helping marketers unlock their true growth potential. Incrementality testing, once complex and limited to expert hands, is now accessible to media buyers at every level. With Stella’s AI on your side, you can confidently refine your marketing strategy and drive real, incremental growth.

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